Note : Not using the updated code, hence I have the lower scores for voxel prediction

Intuitive Explanation

Experiment - Changing n_points in Pointcloud

The decoder architecture for my Image2Pointcloud is as follows -
LinearLayer (512, n_points) -> Relu -> Linear(n_points, n_points * 3) -> Tanh

Since my inner layer was dependent on n_points, so I decided to play around with changing n_points and see how it affects the results.

We can see that as the number of points increases, the performance of the model increases as well.
I interpret it as follow, as the number of points increases, the number of connections in the hidden layer increasing making the network more expressive.

Performed an expertiment where I trained a PointCloud Prediction network on the full dataset.

Qualitative Results -

Quantitative Results -
avg F1 @ 0.05 = 91.45%

A couple of things I noticed, I had to train for nearly around 20000 iterations for the full the loss to converge which was nearly thrice the number of iterations it took when training on one class.
Qualitatively the predictor seems to do better on the Planes and Cars class rather than the chairs.